Challenges and solutions for vision-based hand gesture interpretation: A review.

Hand gesture is one of the most efficient and natural interfaces in current human–computer interaction (HCI) systems. Despite the great progress achieved in hand gesture-based HCI, perceiving or tracking the hand pose from images remains challenging. In the past decade, several challenges have been indicated and explored, such as incomplete data issue, the requirement of large-scale annotated dataset, and 3D hand pose estimation from monocular RGB image; however, there is a lack of surveys to provide comprehensive collection and analysis for these challenges and corresponding solutions. To this end, this paper devotes effort to the general challenges of hand gesture interpretation techniques in HCI systems based on visual sensors and elaborates on the corresponding solutions in current state-of-the-arts, which can provide a systematic reminder for practical problems of hand gesture interpretation. Moreover, this paper provides informative cues for recent datasets to further point out the inherent differences and connections among them, such as the annotation of objects and the number of hands, which is important for conducting research yet ignored by previous reviews. In retrospect of recent developments, this paper also conjectures what the future work will concentrate on, from the perspectives of both hand gesture interpretation and dataset construction. • Analyzing 200+ hand gesture interpretation studies in recent seven years. • Concluding seven challenges and corresponding solutions for these studies. • Providing informative cues of commonly used datasets. • Suggesting potential directions of datasets and methodologies for future research. [ABSTRACT FROM AUTHOR]